--- license: cc-by-4.0 language: - en task_categories: - tabular-classification - tabular-regression multilinguality: monolingual size_categories: - n<1K tags: - "africa" - "electric-sheep-africa" - "open-data" - "metadata-backed" - "health" - "parquet" - "text" - "humanitarian" - "hdx" - "disease" - "education" - "who-is-doing-what-and-where-3w-4w-5w" - "lbr" pretty_name: "Liberia Education 3W | Africa (original)" --- # Liberia Education 3W | Africa (original) **Size category:** `n<1K` - **Formats:** `parquet` - **Sector:** health - *Engineered by [Electric Sheep Africa](https://huggingface.co/electricsheepafrica)* ![size](https://img.shields.io/badge/size-n%3C1K-blue) ![sector](https://img.shields.io/badge/sector-health-green) ![downloads](https://img.shields.io/badge/HF_downloads-16-orange) ![license](https://img.shields.io/badge/license-cc--by--4.0-lightgrey) ## TL;DR This dataset is part of the Electric Sheep Africa catalog on Hugging Face. It is indexed for African data discovery with standardized metadata, loading guidance, provenance notes, and analyst-oriented context. ## What This Dataset Covers Health datasets help researchers examine disease burden, service delivery, risk factors, outcomes, and public-health program performance. Dataset context from the existing Hugging Face card: Liberia Education 3W Publisher: Liberia Education in Emergencies (inactive) · Source: HDX · License: cc-by-igo · Updated: 2023-03-27 Abstract This dataset contains the Who What Where (3W) dataset for the Education sector in Liberia. Each row in this dataset represents first-level administrative unit observations. Data was last updated on HDX on 2023-03-27. Geographic scope: LBR. Curated into ML-ready Parquet format by Electric Sheep Africa. Dataset… See the full description on the dataset page: https://huggingface.co/datasets/electricsheepafrica/africa-liberia-education-3w. ## Dataset Profile | Field | Value | |---|---| | Hugging Face repo | [`electricsheepafrica/africa-liberia-education-3w`](https://huggingface.co/datasets/electricsheepafrica/africa-liberia-education-3w) | | Sector | health | | Topic tags | humanitarian, hdx, electric-sheep-africa, disease, education, who-is-doing-what-and-where-3w-4w-5w, lbr | | Modalities | `text` | | Formats | `parquet` | | Size category | `n<1K` | | Countries | Liberia | | ISO3 coverage | `LBR` | | Last modified on HF | `2026-04-13 00:46:38+00:00` | | Inventory snapshot | `2026-07-16T16:00:34Z` | ## How To Read This Dataset - Start from the repository files and the dataset viewer when available. - Treat the README context as a fast orientation layer; confirm variable definitions and units in the data files before modeling. - Use explicit country columns when present. When geography is only implied by the title or source metadata, document that assumption in downstream analysis. - Preserve missing values until you have a defensible imputation rule. ## Usage ```python from datasets import load_dataset ds = load_dataset("electricsheepafrica/africa-liberia-education-3w") print(ds) split_name = next(iter(ds)) table = ds[split_name] print(table.features) print(table[:3]) ``` ### Convert To Pandas When Tabular ```python from datasets import Dataset first_split = ds[next(iter(ds))] if isinstance(first_split, Dataset): df = first_split.to_pandas() print(df.head()) ``` ## Data Quality Notes - This card was standardized from the Electric Sheep Africa Hugging Face metadata inventory. - Exact schema, row counts, and source files should be inspected in the repository data files. - Metadata gaps from the inventory: upstream_publisher. - Do not infer policy meaning from labels alone; confirm definitions, units, and methods in the source material. ## Source And Provenance - **Source context:** original - **Publisher/source attribution:** original - **License:** [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/) - **Hugging Face URL:** [https://huggingface.co/datasets/electricsheepafrica/africa-liberia-education-3w](https://huggingface.co/datasets/electricsheepafrica/africa-liberia-education-3w) - **Inventory retrieved at:** `2026-07-16T16:00:34Z` ## Suggested Analyses - Inspect schema and missingness before modeling. - Profile variables by geography, time, and subgroup columns where present. - Join with other Electric Sheep Africa datasets using explicit country, year, and indicator fields when available. - Build reproducible notebooks that cite both the original source context and the Electric Sheep Africa Hugging Face repo. ## Citation ```bibtex @misc{electric_sheep_africa_africa_liberia_education_3w_2026, title = {Liberia Education 3W | Africa (original)}, author = {original}, year = {2026}, url = {https://huggingface.co/datasets/electricsheepafrica/africa-liberia-education-3w}, publisher = {Hugging Face Datasets, engineered by Electric Sheep Africa}, howpublished = {\url{https://huggingface.co/datasets/electricsheepafrica/africa-liberia-education-3w}} } ``` ## License Released under [CC BY 4.0](https://creativecommons.org/licenses/by/4.0/). Original source rights remain with the original publisher or data provider. Electric Sheep Africa engineering standardizes discovery metadata, documentation, and usage guidance for analysis on Hugging Face. ## About Electric Sheep Africa Electric Sheep Africa publishes ML-ready African public datasets on Hugging Face. --- Provenance: metadata-backed README standardized 2026-08-12 by the Electric Sheep Africa README system. Inventory source: `catalog/esa_metadata_inventory/master_metadata.jsonl`.